Figure 2.

ROC analysis measuring the performance of CAPPIC in comparison to the methods by Goldberg and Roth and Kuchaiev et al. False positive rate (1-specificity) is plotted against true positive rate (sensitivity) for each of the six reference networks. Since the definition of a negative interaction set in the performance assessment involves a random process, the ROC plots summarize the outcome of 100 runs. Plots show the average ROC curves (thick lines), their standard error bands (dotted lines), as well as the mean area under the ROC curve (AUC) of all runs. The ‘X’-marks on the green ROC curves correspond to the fraction of true/false interactions whose proteins share network neighbors and are thus scored by Goldberg and Roth’s method.

Kamburov et al. BMC Bioinformatics 2012 13:262   doi:10.1186/1471-2105-13-262
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